def _add_training_data(self, X: modALinput, y: modALinput) -> None:
"""
Adds the new data and label to the known data, but does not retrain the model.
Args:
X: The new samples for which the labels are supplied by the expert.
y: Labels corresponding to the new instances in X.
Note:
If the classifier has been fitted, the features in X have to agree with the training samples which the
classifier has seen.
"""
check_X_y(X, y, accept_sparse=True, ensure_2d=False, allow_nd=True, multi_output=True, dtype=None,
force_all_finite=self.force_all_finite)
if self.X_training is None:
self.X_training = X
self.Xt_training = self.transform_without_estimating(self.X_training) if self.on_transformed else None
self.y_training = y
else:
try:
self.X_training = data_vstack((self.X_training, X))
self.Xt_training = data_vstack((
self.Xt_training,
self.transform_without_estimating(X)
)) if self.on_transformed else None
self.y_training = data_vstack((self.y_training, y))
except ValueError:
raise ValueError('the dimensions of the new training data and label must'
'agree with the training data and labels provided so far')
def ranked_batch(classifier: Union[BaseLearner, BaseCommittee],
unlabeled: modALinput,
uncertainty_scores: np.ndarray,
n_instances: int,
metric: Union[str, Callable],
n_jobs: Union[int, None]) -> np.ndarray:
"""
Query our top :n_instances: to request for labeling.
Refer to Cardoso et al.'s "Ranked batch-mode active learning":
https://www.sciencedirect.com/science/article/pii/S0020025516313949
Args:
classifier: One of modAL's supported active learning models.
unlabeled: Set of records to be considered for our active learning model.
uncertainty_scores: Our classifier's predictions over the response variable.
n_instances: Limit on the number of records to query from our unlabeled set.
metric: This parameter is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`.
n_jobs: This parameter is passed to :func:`~sklearn.metrics.pairwise.pairwise_distances`.
Returns:
The indices of the top n_instances ranked unlabelled samples.
"""
# Make a local copy of our classifier's training data.
# Define our record container and record the best cold start instance in the case of cold start.
# transform unlabeled data if needed
if classifier.on_transformed:
unlabeled = classifier.transform_without_estimating(unlabeled)
if classifier.X_training is None:
best_coldstart_instance_index, labeled = select_cold_start_instance(X=unlabeled, metric=metric, n_jobs=n_jobs)
instance_index_ranking = [best_coldstart_instance_index]
elif classifier.X_training.shape[0] > 0:
labeled = classifier.Xt_training[:] if classifier.on_transformed else classifier.X_training[:]
instance_index_ranking = []
# The maximum number of records to sample.
ceiling = np.minimum(unlabeled.shape[0], n_instances) - len(instance_index_ranking)
# mask for unlabeled initialized as transparent
mask = np.ones(unlabeled.shape[0], np.bool)
for _ in range(ceiling):
# Receive the instance and corresponding index from our unlabeled copy that scores highest.
instance_index, instance, mask = select_instance(X_training=labeled, X_pool=unlabeled,
X_uncertainty=uncertainty_scores, mask=mask,
metric=metric, n_jobs=n_jobs)
# Add our instance we've considered for labeling to our labeled set. Although we don't
# know it's label, we want further iterations to consider the newly-added instance so
# that we don't query the same instance redundantly.
labeled = data_vstack((labeled, instance))
# Finally, append our instance's index to the bottom of our ranking.
instance_index_ranking.append(instance_index)
# Return numpy array, not a list.
return np.array(instance_index_ranking)
def expected_error_reduction(learner: ActiveLearner, X: modALinput, loss: str = 'binary',
p_subsample: np.float = 1.0, n_instances: int = 1,
random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
Expected error reduction query strategy.
References:
Roy and McCallum, 2001 (http://groups.csail.mit.edu/rrg/papers/icml01.pdf)
Args:
learner: The ActiveLearner object for which the expected error
is to be estimated.
X: The samples.
loss: The loss function to be used. Can be 'binary' or 'log'.
p_subsample: Probability of keeping a sample from the pool when
calculating expected error. Significantly improves runtime
for large sample pools.
n_instances: The number of instances to be sampled.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The indices of the instances from X chosen to be labelled;
the instances from X chosen to be labelled.
"""
assert 0.0 <= p_subsample <= 1.0, 'p_subsample subsampling keep ratio must be between 0.0 and 1.0'
assert loss in ['binary', 'log'], 'loss must be \'binary\' or \'log\''
expected_error = np.zeros(shape=(len(X), ))
possible_labels = np.unique(learner.y_training)
try:
X_proba = learner.predict_proba(X)
except NotFittedError:
# TODO: implement a proper cold-start
return 0, X[0]
cloned_estimator = clone(learner.estimator)
for x_idx, x in enumerate(X):
# subsample the data if needed
if np.random.rand() <= p_subsample:
# estimate the expected error
for y_idx, y in enumerate(possible_labels):
X_new = data_vstack((learner.X_training, x.reshape(1, -1)))
y_new = data_vstack((learner.y_training, np.array(y).reshape(1, )))
cloned_estimator.fit(X_new, y_new)
refitted_proba = cloned_estimator.predict_proba(X)
if loss is 'binary':
loss = _proba_uncertainty(refitted_proba)
elif loss is 'log':
loss = _proba_entropy(refitted_proba)
expected_error[x_idx] += np.sum(loss)*X_proba[x_idx, y_idx]
else:
expected_error[x_idx] = np.inf
if not random_tie_break:
query_idx = multi_argmax(expected_error, n_instances)
else:
query_idx = shuffled_argmax(expected_error, n_instances)
return query_idx, X[query_idx]